Macroalgal (Enteromorpha spp. and Ulva spp.) Primary Productivity in the Ria Formosa Lagoon

Dalila do Rosário Encarnação Serpa

Licenciada em Biologia Aplicada aos Recursos Animais – Variante Recursos Marinhos

Macroalgal (Enteromorpha spp. and Ulva spp.)

Primary Productivity in the Ria Formosa Lagoon

Dissertação para obtenção do grau de Mestre em Ecologia, Gestão e Modelação dos Recursos Marinhos

Orientador:

Professor Doutor João Pedro Salgueiro Gomes Ferreira,

Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa

Júri:

Presidente: Professor Doutor João Gomes Ferreira Vogais: Professora Doutora Alice Newton

Professora Doutora Maria Helena Ferrão Ribeiro da Costa

Dalila do Rosário Encarnação Serpa

Licenciada em Biologia Aplicada aos Recursos Animais – Variante Recursos Marinhos

Macroalgal (Enteromorpha spp. and Ulva spp.)

Primary Productivity in the Ria Formosa Lagoon

Dissertação para obtenção do grau de Mestre em Ecologia, Gestão e Modelação dos Recursos Marinhos

Orientador:

Professor Doutor João Pedro Salgueiro Gomes Ferreira,

Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa

Júri:

Presidente: Professor Doutor João Gomes Ferreira Vogais: Professora Doutora Alice Newton

Professora Doutora Maria Helena Ferrão Ribeiro da Costa

i

Macroalgal (

Enteromorpha

spp. and

Ulva

spp.) Primary Productivity in the

Ria Formosa Lagoon

Copyright  Dalila do Rosário Encarnação Serpa, FCT/UNL, UNL

iii

Agradecimentos

Ao Professor Doutor João Gomes Ferreira, orientador deste trabalho, pela disponibilidade que demonstrou em esclarecer as minhas dúvidas, pelos comentários e pela oportunidade de participar no Projecto OARRE.

À Doutora Manuela Falcão, pela disponibilização dos meios necessários à realização deste trabalho, e pelas sugestões que me deu no decorrer do mesmo.

Ao Engenheiro Carlos Vale, pelo apoio quando foi solicitado a utilização das instalações do IPIMAR para a realização deste trabalho.

À Milu e à Cristina, pelo apoio que me deram no trabalho de campo e no trabalho laboratorial, e ainda pelo bom ambiente de trabalho que me proporcionaram.

Aos meus pais, por sempre me apoiarem e respeitarem a minhas decisões, e ainda por me “aturarem” todos estes anos.

Às minhas irmãs, Catarina e Carmo, pela amizade e pela força que sempre me deram.

v

Resumo

Em áreas sujeitas a eutrofização, as macroalgas oportunistas de crescimento rápido e elevadas taxas de consumo de nutrientes, podem tornar-se os principais produtores primários do ecossistema, pelo que se torna de grande importância a avaliação correcta da produtividade anual destas algas. Experiências de incubação in situ foram realizadas de modo a determinar quais os factores

ambientais que controlam a produtividade das espécies de Enteromorpha e Ulva na laguna da Ria

Formosa. A análise de regressão revelou que o padrão de variação sazonal da produtividade das algas está significativamente (p < 0.05) relacionado com a intensidade luminosa, sendo que as taxas

fotossintéticas mais elevadas são observadas durante o período de outono/principio de Inverno. Em laboratório, foram também realizadas experiências de incubação de modo a determinar os parâmetros das curvas P-I para estas algas. Em ambas as experiências, in situ e em laboratório,

observou-se fotoinibição a intensidades luminosas mais elevadas. A taxa máxima de produção para a Ulva foi de 3.66 mg C g-1 dw h-1, enquanto que para a Enteromorpha o valor deste parâmetro foi

de 3.55 mg C g-1 _{dw h}-1_{. Os valores da intensidade luminosa óptima para a fotossíntese eram}

semelhantes para a Enteromorpha (47 µE m-2 s-1) e para a Ulva (35 µE m-2 s-1). A relação entre a

luz e as taxas de consumo de nutrientes das macroalgas foi também estudada em laboratório, contudo esta relação não foi significativa (p > 0.05), provavelmente devido ao facto destas algas

oportunistas apresentarem “consumo de luxo”. As taxas respiratórias da Enteromorpha variaram

entre 0.04 e 0.25 mg C g-1 _{dw h}-1_{, enquanto que para a Ulva os valores foram ligeiramente}

superiores, variando entre 0.08 e 0.35 mg C g-1 _{dw h}-1_{. Para simular a produtividade anual das}

macroalgas dominantes na laguna, desenvolveu-se um modelo ecológico. O modelo incluiu a inibição pela luz e a limitação de nutrientes tendo em conta as concentrações internas de azoto e fósforo. Para ambas as algas observou-se que o azoto não era um nutriente limitativo para a fotossíntese, pois ao longo do ano as concentrações internas deste nutriente foram sempre superiores à quota interna mínima. Contudo, observou-se que as concentrações internas de fósforo diminuíam durante o período de Inverno provavelmente devido à baixa disponibilidade de fósforo dissolvido na água. Os valores de produção primária bruta anual estimados pelo modelo são de 190 g C m-2 _ano-1 _{para a}

Enteromorpha e de 132 g C m-2 ano-1 para a Ulva. Anualmente estas algas

contribuem com cerca de 446 ton de carbono para o ecossistema, dos quais 85% provêem das espécies de Enteromorpha. As macroalgas bentónicas são também importantes na remoção de

vi

Palavras-Chave:

vii

Abstract

In areas undergoing eutrophication, opportunistic macroalgae with high nutrient uptake rates and rapid growth may become the dominant primary producers of the ecosystem, revealing the importance of an accurate evaluation for the annual algal production. In order to determine the environmental factors controlling Enteromorpha and Ulva species productivity in the Ria Formosa

lagoon, in situ short-term incubation experiments were performed. Regression analysis revealed

that light seems to accurately (p<0.05) explain the pattern of seasonal variation of algae

productivity, with highest photosynthetic rates during the autumn/early winter period. Laboratory incubation experiments were performed to determine P-I curves parameters for both species. In

both in situ and laboratory experiments, seaweed exhibited photoinhibition at high light intensities.

Maximum production rate for Ulva was 3.66 mg C g-1 dw h-1, while for Enteromorpha it was 3.55

mg C g-1 _{dw h}-1_{. Similar values of optimal light intensity were estimated for Enteromorpha (47 µE}

m-2 _s-1_{) and}

Ulva (35 µE m-2 s-1) species. The relationship between macroalgalnutrient uptake rates

and light irradiances were also evaluated in the laboratory experiments, but the results of regression analysis showed that it was not significant (p > 0.05), probably due to “luxury consumption” by

these opportunistic macroalgae. Enteromorpha respiratory rates ranged from 0.04 to 0.25 mg C g-1

dw h-1_{, while for}

Ulva respiration values were slightly higher varying between 0.08 and 0.35 mg C

g-1 dw h-1. An ecological model was developed in order to simulate the annual productivity of the dominant macroalgae species in the lagoon. The model included photoinhibition and nutrient limitation based on the internal concentration of nitrogen and phosphorus. For both seaweeds there was no nitrogen limitation for seaweed productivity. Over the year, internal nitrogen concentrations were always higher than the minimum internal quota. However, internal phosphorus concentrations were depleted during the winter period probably due to the low availability of dissolved phosphorus. Values of annual gross primary production estimated by the model are higher for

Enteromorpha spp. (190 g C m-2 y-1) than for Ulva spp. (132 g C m-2 y-1). The seaweeds

contribution to the lagoon carbon budget is of 446 ton C y-1_{,from which 85 % correspond to} Enteromorpha species. Benthic macroalgae also play an important role in nutrient removal.

Annually, these algae remove about 69 tons of nitrogen and 10 tons of phosphorus. These quantities correspond in terms of population equivalents to 15700 inhabitants for nitrogen and 10000 inhabitants for phosphorus.

ix

Table of Contents

Agradecimentos

iii

Resumo

v

Palavras-Chave

vi

Abstract

vii

Keywords

vii

Table of Contents

ix

List of Figures

xiii

List of Tables

xv

List of Symbols

xvii

Chapter I – General Introduction

1

1.

General Introduction

3

2. Objectives

5

Chapter II – Site Description

7

Site description

9

1. Climate

10

1.1 Temperature

10

1.2 Rainfall

11

1.3 Solar radiation and insolation

11

1.4 Wind

12

2. Hydrodynamics

12

2.1 Currents

12

2.2 Tides

12

3. Sediment characteristics

13

4. Physico-chemical parameters

13

4.1 Temperature, dissolved oxygen, pH, salinity, Secchi disk and suspended

particulate matter

13

4.2 Nutrients

14

5. Vegetation cover

15

5.1 Macroalgae

16

x

5.1.2 Seasonal distribution

17

Chapter III - Macroalgal Primary Productivity

19

1. Introduction

21

2. State of the art

21

3. Methodology

23

3.1

In situ

incubation experiments

23

3.2 Laboratory incubation experiments

24

3.3 Sampling analysis

25

3.3.1 Dissolved oxygen determination

25

3.3.2 Nutrient determination

25

3.4 Primary productivity calculations

25

3.5

P-I

curves

27

4. Results and Discussion

28

4.1

In situ

incubation experiments

28

4.1.1 Environmental parameters

28

4.1.2 Primary Productivity

28

4.1.3 Respiratory rates

31

4.2 Laboratory incubation experiments

32

4.2.1 Primary Productivity

32

4.2.2 Respiratory rates

33

4.2.3

P-I

curves

34

4.2.4 Nutrient uptake rates

36

4.3. Comparison with other studies

38

Chapter IV – Macroalgal model

41

1. Introduction

43

2. Description of the model

43

2.1 Background

43

2.2 Choice of forcing functions

44

2.3 Model development

44

2.3.1 Gross primary productivity

45

2.3.1.1 Forcing functions and limitations

45

2.3.1.1.1 Light

45

xi

2.3.1.1.1.2 Light at the bottom

47

2.3.1.1.1.3 Tide simulation

47

2.3.1.1.1.4 Light limitation

48

2.3.1.1.2 Nutrients

49

2.3.1.1.2.1 Nutrient uptake

50

2.3.1.1.2.2 Nutrient feedback control

51

2.3.1.1.2.3 Nutrient limitation

51

2.3.2. Respiration

52

2.3.3. Mortality

52

2.4. Simulations

52

2.5. Model implementation

52

2.6. Macroalgal productivity in the Ria Formosa

52

3. Results and Discussion

52

3.1. Internal nutrient concentrations

53

3.2. Primary productivity

58

3.3. Carbon and nutrient budgets

59

Chapter V – General Conclusions

61

General Conclusions

63

xiii

List of Figures

Figure 2.1 - Geographic location of the Ria Formosa lagoon. 9

Figure 2.2 - Mean annual temperature (ºC) in the Ria Formosa lagoon [2]. 11

Figure 2.3 - Mean annual values of rainfall (mm y-1_{) in the Ria Formosa lagoon [2]. 11}

Figure 2.4 - Mean annual values of solar radiation (kcal cm-2_{) in the Ria Formosa lagoon [2]. 12}

Figure 2.5 – GIS maps of the DAIN:Phosphate ratio in the Ria Formosa lagoon. (A) Summer conditions: (I) low, (II) high water; (b) winter conditions: (I) low, (II) high water (Newton et

al., 2003). 15

Figure 2.6 – Macroalgae species in the Ria Formosa lagoon: a) Ulva lactuca, b) Enteromorpha

compressa. 16

Figure 2.7 - Average monthly biomasses (g dw m-2_{) of}

Enteromorpha and Ulva species in the Ria

Formosa lagoon (Adapted from Aníbal, 1998). 17

Figure 3.1 - General scheme of a P-I curve (Parsons et al., 1984). 22

Figure 3.2 - Mean values (± standard deviation) of net (Pn) and gross primary productivity (Pg) for

Enteromorpha over the experimental period. 29

Figure 3.3 – Mean values (± standard deviation) of net (Pn) and gross primary productivity (Pg) for

Ulva over the experimental period. 29

Figure 3.4 - Relationship between water temperature and net primary productivity (Pn) for

Enteromorpha (a) and Ulva (b), with the regression lines and equations. 30

Figure 3.5 - Relationship between light irradiances and net primary productivity (Pn) for

Enteromorpha (a) and Ulva (b), with the regression lines and equations. 31

Figure 3.6 – Enteromorpha spp. P-I curve. Filled circles represent the results obtained in the

laboratory incubation experiments. 35

Figure 3.7 – Ulva spp. P-I curve. Filled circles represent the results obtained in the laboratory

incubation experiments. 35

Figure 4.1 – Annual variation of light intensity (I, µE m-2 _s-1_{). 46}

Figure 4.2 – Annual variation of photoperiod. 46

Figure 4.3 – Tidal height over a spring/neap tidal cycle. 48

Figure 4.4 – Annual variation of total inorganic nitrogen (TIN) concentrations in Ria Formosa. 50

Figure 4.5 – Annual variation of phosphate concentrations in Ria Formosa. 50

Figure 4.6 – Annual variation of internal phosphorus (N) concentrations for Enteromorpha spp. 56

Figure 4.7 – Annual variation of internal phosphorus (P) concentrations for Enteromorpha spp. 56

Figure 4.8 – Annual variation of internal nitrogen (N) concentrations for Ulva spp. 57

Figure 4.9– Annual variation of internal phosphorus (P) concentrations for Ulva spp. 57

xv

List of Tables

Table 2.1 – Mean annual values of grain size and calcimetric analysis of superficial sediments in stations located near the inlets/main channels and inner parts of lagoon/intertidal areas

(adapted from Monteiro, 1989). 13

Table 3.1 - Mean values (± standard deviation) of environmental parameters measured during the

in situ productivity experiments. 28

Table 3.2 - Mean (± standard deviation) respiratory rates of Enteromorpha and Ulva measured in

the in situ incubation experiments. 31

Table 3.3 – Average net primary productivity (Pn) of Enteromorpha spp. for each light incubation

experiment. C.V. – variation coefficient. 32

Table 3.4 – Average net primary productivity (Pn) of Ulva spp. for each light incubation

experiment. C.V. – variation coefficient. 33

Table 3.5 – Mean respiratory rates of Enteromorpha and Ulva with different incubated biomasses.

C.V. – variation coefficient. 34

Table 3.6 – Enteromorpha spp. and Ulva spp. P-I curves parameters. 36

Table 3.7 – Ammonium (NH4+), nitrate (NO3-), nitrite (NO2-) and phosphate (HPO42-) uptake rates

for Enteromorpha and Ulva, at different irradiances. 37

Table 3.8 – Relationship between Enteromorpha nutrient uptake rates and light. In the linear

regressions, the nutrient uptake rate (µM g-1 _{fw h}-1_{) is the dependent variable (y) and}

irradiance is the independent variable (x). 38

Table 3.9 – Relationship between Ulva nutrient uptake rates and light. In the linear regressions,

nutrient uptake rate (µM g-1 _{fw h}-1_{) is the dependent variable (y) and irradiance is the}

independent variable (x). 38

Table 4.1 – Model parameters for Enteromorpha spp. 54

Table 4.2 – Model parameters for Ulva spp. 55

Table 4.3 – Annual gross primary production (Pg), net primary production (Pn), and turnover rates for each seaweed species. Data on biomass was obtained from Aníbal (1998) based on

monthly measurements over a year. 58

Table 4.4 – Annual gross primary production (Pg) and net primary production (Pn) calculated by the model, when a Michaelis-Menten type equation is used for light limitation. 59 Table 4.5 – Total yearly nitrogen and phosphorus removal by Enteromorpha and Ulva species in

xvii

List of Symbols

RF – Ria Formosa

k – light extinction coefficient

SPM – suspended particulate matter

DAIN – dissolved available inorganic nitrogen P – photosynthesis

I – light intensity

Pn – net primary productivity

Pg – gross primary productivity

R – respiration

Pmax – maximum production rate α - initial slope

Iopt – optimal light intensity

Ik – saturating irradiance

Ic – compensation light intensity

PAR – Photosynthetically Active Radiation NH4+ – ammonium

NO3-2 – nitrate

NO2- – nitrite

HPO4-2 – phosphate

dw – dry weight

C.V. – variation coefficient fw – fresh weight

B – biomass

M – mortality

I0– light energy available at water surface

Iz – light available for photosynthesis at depth z

z – depth

h – height of the macroalgae stand above datum

HT – high tide LT – low tide t – time

xviii

N – internal nitrogen concentration P – internal phosphorus concentration

Nup – nitrogen uptake

Nfb – nitrogen feedback control

Pup – phosphorus uptake

Pfb – phosphorus feedback control

VNmax – maximum uptake rate for nitrogen

TIN – total inorganic nitrogen

KN – half saturation constant for nitrogen

VPmax – maximum uptake rate for phosphorus

KP – half saturation constant for phosphorus

QNmax – maximum internal quota for nitrogen

QNmin – minimum internal quota for nitrogen

QPmax – maximum internal quota for phosphorus

QPmin – minimum internal quota for phosphorus

Maxmort – maximum mortality rate

Chapter I

Chapter I

3

1.

General Introduction

In recent years, the quality of coastal waters worldwide has deteriorated as a result of an increase of human population and activities along coastal regions (Newton et al., 2003). Anthropogenic

activities, such as deforestation, agriculture, animal rearing and wastewater treatment, increase the nutrient supply to coastal ecosystems leading to eutrophication (Fong et al., 1994).

Under eutrophic conditions, an excessive growth of seaweeds is observed (Morand and Briand, 1996) and these algae may become the dominant primary producers of the ecosystem (Peckol and Rivers, 1996; Kinney and Roman, 1998; Martins et al., 2001), outcompeting other species, such as

seagrasses and phytoplankton. The competition for nutrients can also control the algal community structure by favouring opportunistic macroalgae species, such as Enteromorpha and Ulva, with

high nutrient uptake rates and rapid growth (Fong et al., 1994; Pedersen and Borum, 1996;

Bachelet et al., 2000) due to a simple thallus morphology (high surface to weight ratios) which

makes them more suited to obtain light energy and nutrients (Littler, 1979) than other benthic macroalgae.

At first, the development of opportunistic macroalgae acts as a bioremediation mechanism (Morand and Briand, 1996) because a large part of the nutrients excess is removed. Then, when the environmental conditions become unfavourable, the plants die and decompose, leading to an increase in the amounts of organic detritus and a decrease in dissolved oxygen concentrations. This degradation of water quality affects the living resources, tourism, recreation, and human and environmental health.

Besides nutrient availability, the macroalgal primary productivity is also determined by other

environmental factors such as light intensity and temperature (Parsons et al., 1984; J∅rgensen,

1994).

In shallow coastal ecosystems such as the Ria Formosa lagoon, where light may reach the bottom, benthic primary producers play an important role in carbon fixation and nutrient removal (NICE, 1999). Thus, the study of macroalgal primary productivity is of special interest once it can be used as a potential indicator of eutrophication (Fong et al., 1994).

Recently, studies carried out in the Ria Formosa lagoon (Newton et al., 2003) demonstrated the

Chapter I

4

(Padinha et al., 2000) there are already signs of eutrophication, as it was documented a decrease in

seagrasses and a large increase in algal mats in the lagoon. The macroalgal community of the Ria Formosa is also dominated by green opportunistic macroalgae, such as the Ulvales, Enteromorpha

spp. and Ulva spp. (Cunha, 1990; Aníbal, 1998), species often found in areas undergoing

eutrophication (Lavery and McComb, 1991; Sfriso and Marcomini, 1997; Martins et al., 2001).

The benthic macroalgae community of the Ria Formosa lagoon is not well studied. Although there are some species inventories (Ardré, 1970; Cunha, 1990; Duarte et al., 1988), studies on seaweeds

taxonomy (Mata, 1997), and on the spatial and seasonal variability of algae biomass over the year (Reis, 1994; Aníbal, 1998), there is a lack of information about macroalgal productivity and their contribution to the overall ecosystem productivity.

Chapter I

5

2.

Objectives

The main objective of the present work was to the estimate the annual productivity of the dominant macroalgae species (Enteromorpha spp. and Ulva spp.) in the Ria Formosa lagoon. This was

approached by:

1) Determining the relative importance of environmental factors (water temperature, light intensity and salinity) controlling macroalgal productivity in the Ria Formosa;

2) Determining the P-I curve parameters for each macroalgae;

3) Developing an ecological model in order to simulate the annual macroalgal productivity at

Chapter II

Chapter II

9

Site description

The Ria Formosa is a shallow mesotidal lagoon located along the eastern part of the south coast of Portugal (Fig. 1.1), with an extension of 55 km (from Ancão to Cacela), a maximum width of 6 km (from Faro to Cape Santa Maria) and a wet area of 105 km2_{. The lagoon is protected from the}

ocean by a sandy barrier island interrupted by six inlets (S.Luís, Faro-Olhão, Armona, Fuzeta, Tavira e Cacela) (Fig. 1.1). It has several channels and an extensive intertidal area, around 50 % of the total wet area (53 km2_{), mostly constituted by sand, muddy-sand flats and salt marshes}

(Bettencourt, 1994).

Figure 2.1 - Geographic location of the Ria Formosa lagoon.

The morphological diversity of this ecosystem determines different environmental units defined according to morphological and phytomorphological criteria (Andrade, 1990). These environmental units are classified as:

Tidal flats establish the connection between salt marshes and tidal channels. Their lower limit is the mean level of the low tides of spring tides. These areas are characterized by silt-clay or muddy-sand bottoms without halophyte vegetation but abundantly covered by the seagrass,

8.05 8.00 7.95 7.90 7.85 7.80 7.75 7.70 7.65 7.60 7.55 7.50

Longitude (ºW)

L

a

ti

tu

d

e

(

ºN

)

I

I

Atlantic Ocean

I

I

I

I

I1 – S. Luís Inlet

I2 – Faro-Olhão Inlet

I3 – Armona Inlet

I4 – Fuzeta Inlet

I5 – Tavira Inlet

I6– Cacela Inlet

Faro

Olhão

Tavira

Marim

Cacela

Ancão

RF

_Fuzeta

Chapter II

10

Zostera noltii. Almost 5 km2 of the tidal flats are used for clam farming, which means removal

of vegetation and a periodical addition of sand.

Salt marshes located above tidal flats cover an area of 34 km2. Silt-clay sediments colonised by halophyte species characterize these areas. The vegetation is dominated by Spartina

maritima, Salicornia nitens, Arthrocnemum perenne, Suaeda maritima and Atriplex portucaloides [1]. The low salt marsh is almost exclusively colonized by Spartina maritima

and the bigger plants colonize the high salt marsh with a shorter immersion period.

Tidal channels allow an easy water circulation. The bottom grain size varies from coarse sand to silt. Large areas of the bottom are covered by Zostera marina, which contributes to

sedimentation of suspended particulate matter.

Lagoon beaches with sand and muddy-sand sediments appear especially within tidal flats and salt marshes.

The Ria Formosa lagoon and some of its hinterland has been included in a National Park (184 km2₎

and accepted as a Natura 2000 network and a Ramsar site, as a recognition of its environmental value. The park includes a great diversity of habitats, including salt marshes (35 km2), dunes (19 km2_{), saltpans (10 km}2_{), fish farms (2.8 km}2_{) and, muddy-sand flats (24 km}2_{) [1]. The lagoon is}

also a valuable resource to the Algarve region for tourism, fisheries, aquaculture and salt extraction.

1. Climate

The climate in the Algarve region is typically Mediterranean, with warm dry summers and mild winters.

1.1 Temperature

Chapter II

11

Figure 2.2 - Mean annual temperature (ºC) in the Ria Formosa lagoon [2].

1.2 Rainfall

In the Ria Formosa, the mean annual values of rainfall vary between 400 and 600 mm y-1 _{(Fig. 1.3).}

Generally, the wettest month is December with about 17% of the total annual rainfall, and the driest months are July and August with less than 1% of the annual rainfall (Falcão, 1997).

Figure 2.3 - Mean annual values of rainfall (mm y-1_{) in the Ria Formosa lagoon [2].}

1.3 Solar radiation and insolation

In the Ria Formosa, solar radiation is high with mean annual values ranging between 161 and 165 kcal cm-2_{, except in the extreme west of the lagoon where values above 165 kcal cm}-2 _{are reached}

(Fig. 1.4). Insolation is also high varying between 3000 and 3200 hours in a year [2].

RF

Chapter II

12

Figure 2.4 - Mean annual values of solar radiation (kcal cm-2_{) in the Ria Formosa lagoon [2].}

1.4 Wind

In the eastern part of the Algarve region where Ria Formosa is located, the wind blows predominantly from west and southwest, however during spring and autumn there is a high incidence of winds blowing from east (Granja, 1984).

2.

Hydrodynamics

As the volume of water exchanged between the lagoon and the sea varies from 30 × 106 _{to 100 ×}

106 m3, according to the tidal amplitude (CEPASA, 1980), and the subtidal water volume is about 50 × 106 _m3_{, it is assumed that at neap and spring tides, 40% to 70% of the water volume is}

respectively exchanged with the sea (Sprung, 1994).

2.1 Currents

In the main channels the current speed is less than 1 m s-1_{, while in the inlets it exceeds 2 m s}-1 _(IH,

2002).

2.2 Tides

In the Ria Formosa, tides exhibit a semi-diurnal regime and a fortnightly cycle of spring and neap tides. The mean tidal range is 2.0 m, ranging from 0.5 m in neap tides to 3.5 m in spring tides (Melo, 1989), thus a rather intense exchange of water mass occurs during each tide (Sprung, 1994)

Chapter II

13

and large intertidal areas are exposed to the atmosphere for several hours over each semi-tidal period.

3. Sediment characteristics

The results of grain size analysis of sub-tidal and intertidal superficial sediments are presented in Table 1.1. Near the inlets and in the main channels of the lagoon submitted to strong currents, sandy sediment prevails, while in the inner parts of lagoon and in the intertidal areas predominates mud or muddy-sand sediments (Granja, 1984; Monteiro, 1989).

Table 2.1 – Mean annual values of grain size and calcimetric analysis of superficial sediments in stations located near the inlets/main channels and inner parts of lagoon/intertidal areas (adapted from Monteiro, 1989).

Stations

Clay (%) Silt (%) Sand (%) CaCO3(%)

Mean s.d. Mean s.d. Mean s.d. Mean s.d.

Inlets / Main channels 0.55 0.39 0.78 0.42 95.58 7.45 5.02 2.34 Inner parts of lagoon / Intertidal

areas 6.18 3.70 32.63 16.21 62.10 15.58 7.00 3.26

4 Physico-chemical parameters

4.1 Temperature, dissolved oxygen, pH, salinity, Secchi disk and suspended

particulate matter

Water temperature exhibits a seasonal fluctuation with a minimum in winter (13 ºC) and a maximum of 26 ºC in summer (Falcão, 1997; Falcão and Vale, 2003).Concentrations of dissolved oxygen are close to saturation values all over the year (Falcão and Vale, 2003; Newton et al.,

2003), probably due to an intense exchange of water between the lagoon and the sea, and to a high primary productivity within the lagoon (Falcão and Vale, 2003). Values of pH vary from 8.0 to 8.6

at neap tides and are slightly lower (≈ 8.2) in spring tides due to mixing with incoming seawater (Falcão and Vale, 2003). Because freshwater inputs to the lagoon are negligible, salinity remains close to 36 (Falcão, 1997), except during sporadic and short periods of winter runoff when it may reach 30 (Falcão et al., 1992). The Secchi disk depth ranges annually from 1.5 m to 3.2 m [3],

which corresponds to an annual range of light extinction coefficient (k), between 0.5 and 1.1 m-1

Chapter II

14

- 45 g m-3) of suspended particulate matter (SPM) observed over the year. SPM regulates the underwater light climate, and therefore determines the benthic primary productivity in shallow coastal systems (Lorenz et al., 1999).

4.2 Nutrients

In the Ria Formosa lagoon, nutrient concentrations exhibit seasonal, spatial and tidal variability (Newton, 1995; Falcão, 1997; Newton et al., 2003).

The average concentrations of dissolved available inorganic nitrogen (DAIN) fluctuate around 20 µM with greater concentrations in the eastern lagoon (10 – 150 µM) compared to the western lagoon (1-35 µM) (Newton et al., 2003). These differences are greater in winter months, with

concentrations in the eastern lagoon increasing up to 150 µM, while in the western lagoon DAIN concentrations are around 35 µM. The high levels of DAIN observed in the eastern lagoon during the rainfall period are probably related to the fact that this area is under the impact of rivers (e.g. Gilão), streams (e.g. Almargem) and agricultural runoff.

Phosphate is generally greater than 0.6 µM and always higher in the eastern lagoon (0.75 – 1.4 µM) compared to the western lagoon (0.35 – 1.3 µM). The highest phosphate concentrations are observed during late spring and early summer, decreasing to below 0.8 µM during late summer and early autumn. Phosphate increases during late autumn and early winter, reaching values of 1.2 µM in the eastern lagoon (Newton et al., 2003).

Nitrate and phosphate concentrations also vary with the tide level (Falcão and Vale, 2003). At high tide the concentrations of these nutrients are higher than at low tide, suggesting that they are imported from the sea, mainly during the period of lower water temperatures (Falcão, 1997).

Based on the distribution of DAIN and phosphate concentrations in the lagoon, Newton et al.

Chapter II

15

Figure 2.5 – GIS maps of the DAIN:Phosphate ratio in the Ria Formosa lagoon. (A) Summer conditions: (I) low, (II) high water; (b) winter conditions: (I) low, (II) high water (Newton et al., 2003).

5. Vegetation cover

Subtidal and intertidal areas of the lagoon are extensively covered by benthic macrophytes, such as macroalgae (Enteromorpha spp. and Ulva spp.), seagrasses (Zostera sp., Cymodocea nodosa and

Ruppia cirrhosa) and Spartina maritima that dominate the low salt marshes (Falcão, 1997).

The intertidal areas are mainly covered by Spartina maritima (8 km2), seagrasses (8.2 km2) and

Chapter II

16

5.1 Macroalgae

In the Ria Formosa lagoon, macroalgae occur mainly in the tidal flats. In these areas, 16 species of Phaeophyta (brown algae), 22 species of Chlorophyta (green algae), and 39 species of Rhodophyta (red algae) were identified (Ardré, 1970; Cunha, 1990; Duarte et al., 1988; Reis, 1994; Mata,

1997). Of these algae, the most representative are the Ulvales, Enteromorpha spp. and Ulva spp.

(Fig. 1.6), contributing to more than 70 % of the seaweed biomass in the lagoon (Reis, 1994).

Figure 2.6 – Macroalgae species in the Ria Formosa lagoon: a) Ulva lactuca, b) Enteromorpha compressa.

Macroalgae are distributed along a horizontal axis. Substrate, temperature, light intensity, nutrient availability and hydrodynamics (tidal range and currents) are the major factors concerning the algae distribution in this lagoon (Cunha, 1990; Duarte et al., 1988).

5.1.1 Spatial distribution

Reis (1994) observed a strong correlation between the Ulvales biomass and substrate, in the eastern part of the lagoon. Intertidal areas of sandy sediments and coarse materials seem to be the more suitable to the establishment of Enteromorpha and Ulva species. In fact, these algae are a major

problem in the growth banks of clams where they have to be constantly removed (Cunha, 1990). However, Enteromorpha species are also common in muddy areas (Reis, 1994).

Although Enteromorpha and Ulva species are frequently observed together (Cunha, 1990; Reis,

1994), it seems that Enteromorpha species are dominant in the upper intertidal areas (Cunha,

1990), at a mean depth of 1.2 m (positive above tidal datum). This is probably due to a higher capacity of resistance to extreme environmental conditions (high temperatures, high light

Chapter II

17

intensities and desiccation) (Pregnall and Rudy, 1985). On the other hand, Ulva species colonize

mainly subtidal and lower intertidal areas (Cunha, 1990), about 0.8 m above tidal datum.

5.1.2 Seasonal distribution

In the Ria Formosa, macroalgae blooms appeared in September, by the time of the first autumn rainfalls and disappeared gradually during the following spring (Fig. 1.7). Minimum values of seaweed biomass were observed in summer (July-August). This pattern of seasonal variation is typical from transitional areas, between temperate and tropical zones (Morand and Briand, 1996).

Over the year, the mean values of Enteromorpha biomasses are higher than for Ulva (Aníbal,

1998). This author has documented a minimum Enteromorpha biomass in August (4.5 g dw m-2)

and a maximum in September (56.1 g dw m-2_{), while for Ulva, biomasses ranged from 1.1 g dw m}-2

in July-August to 10.2 g dw m-2 _{in February (Fig. 1.7).}

Figure 2.7 - Average monthly biomasses (g dw m-2_{) of Enteromorpha and Ulva species in the Ria Formosa}

lagoon (Adapted from Aníbal, 1998). 0 10 20 30 40 50 60 Fe b M ar A br M

ay _Jun

Ju

ly

A

ug _Sep _Oct

N

ov _Dec _Jan

Chapter III

CHAPTER III.

Chapter III

21

1. Introduction

Benthic macroalgae are major contributors to the primary productivity of coastal ecosystems (Mann, 1973; Pregnall and Rudy, 1985; D'Avanzo et al., 1996; Peckol and Rivers, 1996; Kinney

and Roman, 1998; Alvera-Azcárate et al., 2003).

Seaweeds productivity is determined by physical (hydrodynamics, light irradiances, temperature), chemical (nutrient availability, salinity, pH) and biological factors, e.g. grazing (Steffensen, 1976; Lapointe and Tenore, 1981; Parsons et al., 1984; Thom and Albright, 1990; Henley et al., 1991;

Fong et al., 1994; Lobban and Harrison, 1994; Duarte and Ferreira, 1995; Valiela, 1995; Van Den

Hoek et al., 1995) that may act synergistically or not.

The aim of this chapter was to study the primary productivity of the most representative species of macroalgae (Enteromorpha spp. and Ulva spp.) in the Ria Formosa lagoon and to evaluate the

influence of environmental factors (temperature and light) on the photosynthetic rates of these seaweeds. Enteromorpha and Ulva photosynthetic rates were studied by generating P-I curves for

each macroalgae genera.

2. State of the art

In situ (Pregnall and Rudy, 1985; Ferreira and Ramos, 1989; Kinney and Roman, 1998) and

laboratory (Arnold and Murray, 1980; Nelson and Siegriest, 1987; Duarte and Ferreira, 1995; Peckol and Rivers, 1996; Hanelt et al., 1997) short-term incubation experiments have been used to

determine macroalgal primary productivity.

Methods for the determination of seaweed photosynthetic rates are usually based onmeasurements of either carbon dioxide consumption or oxygen production per unit time (Lobban and Harrison, 1994; Van Den Hoek et al., 1995).

CO2 is usually measured by the infra-red analysis method or the radiocarbon (14C) method (Van

Den Hoek et al., 1995). The infra-red analysis method consists in measuring the difference in CO2

concentration before and after its introduction in the photosynthesis chamber (Van Den Hoek et al.,

1995). In the radiocarbon method, a high specific-radioactivity isotope (H14_CO

3) is added to the

incubation vessels and the proportion of total radioisotope incorporated into the seaweed is measured (Lobban and Harrison, 1994; Van Den Hoek et al., 1995). This method is usually used in

Chapter III

22

should be taken in interpreting the results obtained by the radiocarbon method because: 1) there is an apparent discrimination in the assimilation rates of the two carbon isotopes (14_CO

2 and 12CO2),

2) the 14_{C-organic carbon may be lost by exudation during the experiment, 3) some} 14_CO 2 fixed

may be lost due to dark respiration or photorespiration, which take place simultaneously with photosynthesis (Parsons et al., 1984).

Productivity estimates based on oxygen evolution can be made using an oxygen electrode or the Winkler method (Lobban and Harrison, 1994; Van Den Hoek et al., 1995). The advantages of the

oxygen methods are that net primary productivity (Pn), gross primary productivity (Pg) and respiration (R) can be directly estimated. However there may be an overestimation of respiration in

the light due to photorespiration. Another disadvantage is that heterotrophic respiration is also included in the measurement. Although one order of magnitude less sensitive than the 14C-method, the oxygen methods are suitable for use in coastal waters (Parsons et al., 1984).

The relationship between photosynthesis (P) and light intensity (I) is fundamental to study

macroalgal productivity. A general scheme of a P-I curve is presented in Fig. 2.1.

Figure 3.1 - General scheme of a P-I curve (Parsons et al., 1984).

Chapter III

23

The initial slope, α, has been defined as the "quantum yield", i.e., the number of moles of oxygen produced (or of carbon incorporated) per unity light intensity. The initial slope is a function of the light reactions and is not usually affected by other environmental factors (Parsons et al., 1984).

Iopt is the optimum light intensity for photosynthesis, while, Ik, the saturating irradiance, is defined as the point at which the extrapolated initial slope intercepts Pmax (Fig. 8). Ik gives a measure of the radiant energy or illumination at light saturation but it does not express photosynthetic efficiency (Parsons et al., 1984; Lobban and Harrison, 1994; Van Den Hoek et al., 1995).

When gross photosynthesis (Pg) equals respiration (R), the net photosynthesis (Pn) is zero and the photosynthetic system is at the compensation point (Van Den Hoek et al., 1995). The light intensity

at the compensation point is called the compensation light intensity, Ic (Parsons et al., 1984).

If organisms are exposed to a strong light above the point at which they are light saturated, the P-I

curve may show a decrease in the photosynthetic rates. This phenomenon is termed photoinhibition (Parsons et al., 1984; Valiela, 1995) and it involves damage to some photosystems components

(especially PS-II), such as membranes or electron-transport proteins, and changes in the enzymatic activity (Neale and Marra, 1985).

Several mathematical models have been proposed for describing the primary productivity of phytoplankton (Steele, 1962; Jassby and Platt, 1976; Platt et al., 1980; Neale and Marra, 1985;

Eilers and Peters, 1988; McBride, 1992;Macedo et al., 1998) and macroalgae (Nelson and Siegrist,

1987; McBride, 1992; Duarte and Ferreira, 1995) as a function of light intensity. The choice of a particular mathematical formulation may lead to different estimates of the P-I curve parameters

therefore it is important for these formulations to accurately describe experimental data in order to obtain reliable primary productivity estimates.

3. Methodology

3.1 In situ incubation experiments

In order to study the seasonal variation of macroalgal primary productivity, in situ short-term

incubation experiments were carried out during Summer, Autumn, Winter and Spring 2001/2002.

Chapter III

24

Water temperature, salinity and dissolved oxygen concentrations were measured using a multiparameter probe (YSI 55). Measurements of photosynthetically active radiation (PAR) were made underwater and in air, using a LICOR LI-250 light meter.

Macroalgal photosynthetic and respiratory rates were determined by the oxygen method (Thomas, 1988). Three incubation bottles were filled with lagoon water; two (light and dark) were incubated containing macroalgae, and a third without algae (control) was used to assess the effect of planktonic primary production on the experimental results. All experiments were made in triplicate.

Before the experiments, lagoon water samples were collected in order to determine the initial oxygen concentration. These samples were immediately fixed according to the Winkler method (Grasshoff, 1983).

Macroalgal samples were washed to remove sediment deposits, epifauna and attachment materials and immediately incubated, in situ, within Winkler bottles of 300 ml.

Incubation times (1 hour) and biomasses (4 to 7 g of fresh weight) were chosen so as to prevent inhibition of photosynthesis by an excess of dissolved oxygen, pH changes or nutrient depletion, and to simultaneously assure that any oxygen changes were detectable (Dromgoole, 1978; Littler, 1979). During incubation, the bottles were rotated at regular intervals to ensure an even exposure to the light. Following the incubation period, water samples for dissolved oxygen determination were immediately fixed and the incubated algae removed from the bottles. Afterwards, algal samples were taken to the laboratory and dried at 80 ºC to determine dry weight (dw).

3.2 Laboratory incubation experiments

Laboratory incubation experiments were performed in order to generate P-I curves for both

macroalgae genera. As for the in situ incubation experiments, photosynthetic and respiratory rates

of seaweeds were determined by the oxygen method.

Before the experiments, water samples were collected and filtered in order to determine the initial oxygen and nutrient (ammonium, nitrate, nitrite and phosphate) concentrations.

Macroalgal samples were incubated in the laboratory using light provided by 1500 W tungsten halogen lamps (Drew, 1983), placed 1 m above the samples. Heat produced by the lamps was dissipated using a cold water flow system (Macedo et al., 1998). Light intensity (0 to 1100 µE m-2

Chapter III

25

PVC nets. In order to determine the effect of incubated algal biomass on photosynthetic rates, two Winkler bottles (300 ml) containing 1 g and 4 g fw of algae, respectively, were incubated for each light intensity. The experiments were carried out in triplicate.

Simultaneously with productivity determinations, the effect of light on macroalgal nutrient uptake rates was also studied. After the incubation period (30 minutes), water samples were collected from the incubation bottles in order to determine macroalgal nutrient consumption.

Macroalgal samples were also incubated in the dark in order to determine respiratory rates.

3.3 Sampling analysis

3.3.1 Dissolved oxygen determination

In both in situ and laboratory experiments, dissolved oxygen concentrations were determined by

the Winkler method (Grasshoff, 1983). All water samples for oxygen determination were analysed in the same day of collection.

3.3.2 Nutrient determination

The ammonium (NH4+), nitrate (NO3-), nitrite (NO2-) and phosphate (HPO42-) concentrations were

determined using a “SKALAR” autoanalyser according to the methods of Technicon Industrial Systems (Grasshoff, 1983).

3.4 Primary productivity calculations

In the in situ incubation experiments, the oxygen variation in the light bottles is a measure of

macroalgal and phytoplankton photosynthetic activity (Pm + Pp). It is also a function of algae

respiration (Rm + Rp), bacterial respiration (Rb) and zooplankton respiration (Rz).

Light bottle (∆ O2) = Pm + Pp – (Rm +Rp + Rb + Rz)

In the dark bottles, the oxygen variation represents the respiration of all the aerobic organisms present.

Chapter III

26

Thus, the results will be representative of a community however, for macroalgae, which represent the bulk of the response, the results will reflect the species chosen (Thomas, 1988). In the present study the values concerning plankton productivity and respiration were very similar to initial oxygen values, and were not used in calculations.

In the laboratory experiments, macroalgae are the only organisms contributing to the oxygen variation in either light or dark bottles, because lagoon water was filtered.

The oxygen variation in light bottles is a measure of macroalgal net primary productivity (Pn). Gross primary productivity (Pg) is the sum between Pn and macroalgal respiration (R).

Pn and R were calculated by the following equation:

Pn (R) = ([O2]final – [O2]initial) × V × F × Q (1)

W × t

Pn – net primary productivity (mg C g-1 dw h-1)

R – respiration (mg C g-1 dw h-1)

[O2] final – dissolved oxygen concentration at the end of the incubation experiment (mg l-1).

[O2] initial - dissolved oxygen concentration before the incubation experiment (mg l-1).

V – volume of the incubation bottle (l).

F – conversion factor of oxygen mass to carbon mass (0.29). Q – photosynthetic/respiratory quotient.

W – macroalgae weight (g dw). t – incubation time (h).

According to the conventionally accepted stoichiometric equation for photosynthesis (Tian et al.,

1993):

106 CO2 + 16 NO3- + HPO42- + 122 H2O + 18 H+→ C106H263O110N16P + 138 O2

the oxygen production can be converted to carbon production by a reduction factor of 0.29.

Values of primary productivity expressed as mg O2 g-1 dw h-1 were converted to mg C, assuming a

Chapter III

27

3.5 P-I curves

In order to generate P-I curves, productivity data obtained in the laboratory incubation experiments

was fitted to the Steele (1962) and Eilers and Peters (1988) mathematical models because these two mathematical formulations account for photoinhibition.

Author Model

Steele (1962)         −

=

I opt

e

I

I

P

P

Eilers and Peeters

(1988)

aI

bI

c

I

P

+

+

=

In Steele’s model, the parameters, I opt and Pmax are directly estimated, while α and Ik were calculated according to the following equations:

α = Pmax













Iopt

e

Ik =

S

P

max

(3)

By differentiating the Eilers and Peters (1988) model, the parameters α, I opt, Pmax and Ik can be expressed as a function of the a, b and c parameters:

α

c

1

=

I opt

a c

= (5)

Pmax =

ac

b 2

1

+ (6)

Ik =

ac b

c

2

Chapter III

28

Macroalgal productivity data were fitted to the mathematical models using non-linear estimation (Statistica software version 5.0).

4. Results and Discussion

4.1 In situ incubation experiments

4.1.1 Environmental parameters

During the in situ productivity incubations, water temperature followed a clear pattern of seasonal

variation (Table 2.1), with lower values during the winter period (January-February) and maximum values during spring/summer. Salinity remained close to 36, as expected because the freshwater supply to the lagoon is irrelevant (Falcão, 1997), except during sporadic periods of intense runoff (Falcão et al, 1992). Dissolved oxygen concentrations were always close to saturation values,

ranging from 6.4 to 9.3 mg l-1_{. Underwater light irradiances ranged from 404 (February) to 1650}

µE m-2 _s-1 _{(August), and were generally lower than air irradiances due to light attenuation in the}

water column (Parsons et al., 1984).

Table 3.1 - Mean values (± standard deviation) of environmental parameters measured during the in situ

productivity experiments.

Date

Water

temperature

Salinity Dissolved

oxygen PAR (µE m-2 s-1)

(ºC) (mg l-1) Air Underwater

21-08-01 23.6 ± 1.2 35.6 ± 0.1 7.1 ± 0.1 1740 ± 158 1650 ± 150 20-09-01 22.8 ± 0.5 35.6 ± 0.1 6.4 ± 0.1 812 ± 187 781 ± 216 16-10-01 19.8 ± 0.4 35.8 ± 0.1 6.9 ± 0.1 850 ± 167 796 ± 106 16-01-02 13.2 ± 0.8 35.6 ± 0.2 9.3 ± 0.1 1060 ± 82 1256 ± 22 28-02-02 16.6 ± 0.1 35.6 ± 0.1 7.2 ± 0.1 415 ± 10 404 ± 34 27-05-02 23.1 ± 1.0 35.6 ± 0.1 8.0 ± 0.1 1964 ± 20 1502 ± 61

4.1.2 Primary productivity

Chapter III

29

Aug Sep Oct Jan Feb May

Month P ( m g C g

-1 d

w h -1 ) Pn Pg 0 1 2 3 4 5 6 7 8

Aug Sep Oct Jan Feb May

Month P ( m g C g

-1 d

w

h

-1 )

Pn Pg

spring-summer (Pregnall and Rudy, 1985; D'Avanzo et al., 1996; Kinney and Roman, 1998), the highest productivity values in the lagoon were observed during autumn and early winter (Fig. 2.2 and 2.3). The maximum net production rates of Enteromorpha (2.5 mg C g-1 dw h-1) and Ulva (6.1

mg C g-1 _{dw h}-1_{) were measured in January, at an ambient water temperature of 13.2 ºC and an}

irradiance of 1256 µE m-2 _s-1_{. The lowest productivity values of}

Enteromorpha (Fig. 2.2) and Ulva

(Fig. 2.3) were measured in February (16.6 ºC; 415 µE m-2 _s-1_{) and May (23.1 ºC; 1964 µE m}-2 _s-1_).

Although the photosynthetic rates of both macroalgae genera presented the same pattern of variation, the results show that, for this range of temperatures and light irradiances, Ulva (2.1 – 6.1

mg C g-1 _{dw h}-1_{) has consistently higher production rates than Enteromorpha (1.2 – 2.5 mg C g}-1

dw h-1_).

Figure 3.2 - Mean values (± standard deviation) of net (Pn) and gross primary productivity (Pg) for Enteromorpha over the experimental period.

Figure 3.3 – Mean values (± standard deviation) of net (Pn) and gross primary productivity (Pg) for Ulva

Chapter III

30

In order to understand the variation of Enteromorpha and Ulva primary productivity, over the

experimental period, a regression analysis was performed to evaluate the relationship between environmental factors and photosynthetic rates. This analysis was only performed for the parameters, water temperature and light irradiances because salinity remained relatively constant over the experimental period (Table 2.1) and dissolved oxygen does not determine photosynthesis. Macroalgal productivity was not significantly (p>0.05) related to water temperature (Fig. 2.4)

possibly because other environmental factors, such as light or nutrient availability, do not allow the expression of temperature limitation (Rivers and Peckol, 1995).

Figure 3.4 - Relationship between water temperature and net primary productivity (Pn) for Enteromorpha (a)

and Ulva (b), with the regression lines and equations.

As shown in Fig. 2.5, light seems to accurately (p<0.05) explain the pattern of variation of seaweed

productivity in the Ria Formosa lagoon. Macroalgal productivity increased with light up to a certain irradiance value. At high light irradiances (1740 - 1964 µE m-2 s-1), the production rates of both Ulvales were inversely proportional to light, suggesting that the algae were photoinhibited (Parsons et al., 1984; Lobban and Harrison, 1994; Van Den Hoek et al., 1995).

Pn = -0,0598x + 2,9467

R2 _{= 0,244}

0 1 2 3 4

0 5 10 15 20 25

Water tem perature (ºC)

Pn = -0,2326x + 8,2531

R2 _{= 0,3128}

0 1 2 3 4 5 6 7 8

0 5 10 15 20 25

Water tem perature (ºC)

a

Chapter III

31

Figure 3.5 - Relationship between light irradiance and net primary productivity (Pn) for Enteromorpha (a)

and Ulva (b), with the regression lines and equations.

4.1.3 Respiratory rates

Macroalgal respiratory rates measured in the in situ incubation experiments are presented in Table

2.2. Enteromorpha and Ulva presented similar respiratory rates, however Ulva respiration values

(0.08 to 0.35 mg C g-1 _{dw h}-1_{) varied within a broader range than for Enteromorpha (0.04 to 0.25}

mg C g-1 _{dw h}-1_).

Table 3.2 - Mean (± standard deviation) respiratory rates of Enteromorpha and Ulva measured in the in situ

incubation experiments.

Date Respiratory rates (mg Cg-1 dw h-1)

Enteromorpha spp. Ulva spp.

21-08-01 0.04 ± 0.01 0.08 ± 0.06 20-09-01 0.21 ± 0.02 0.18 ± 0.02 16-10-01 0.13 ± 0.02 0.10 ± 0.09 16-01-02 0.23 ± 0.05 0.21 ± 0.01 28-02-02 0.25 ± 0.06 0.22 ± 0.17 27-05-02 0.20 ± 0.04 0.35 ± 0.04

Pn = -2E-06x2 _{+ 0,0043x - 0,4158}

R2 _{= 0,64}

0 1 2 3 4

0 500 1000 1500 2000

Irradiance (µE mPn = -6E-06x-2 _s-1₎

2 _{+ 0,0135x - 2,5294}

R2 _{= 0,7361}

0 1 2 3 4 5 6 7 8

0 500 1000 1500 2000

Irradiance (µE m-2 _s-1₎

a

Chapter III

32

4.2 Laboratory incubations experiments

In the laboratory, incubation experiments were performed for different light irradiances in order to generate P-I curves for each macroalgae genera.

4.2.1 Primary productivity

Net productivity values of Enteromorpha (Table 2.3) and Ulva (Table 2.4) varied with the

incubated biomass. Correlation analysis revealed that, for both genera, production rates were significantly lower (p< 0.01) in the incubation bottles containing higher alga biomasses (4g fw).

This may have happened because the use of freshly cut pieces of alga thallus normally generates an increase in thallus respiration due to tissue damage (Drew, 1983; Lobban and Harrison, 1994), and consequently photorespiration is overestimated and net photosynthesis underestimated (Parsons et

al., 1984). The productivity results used to generate P-I curves were the ones concerning to the

experiments in which 1g fw of algal biomass was incubated.

Independently from the incubated biomass, the photosynthetic rates of both macroalgae species exhibited a similar pattern of variation with light. Seaweed productivity values increased with increasing irradiances, but at high light levels (1100 µE m-2 _s-1_{), production rates were reduced}

probably due to photoinhibition (Parsons et al., 1984; Lobban and Harrison, 1994; Van Den Hoek

et al., 1995), as observed in the in situ incubation experiments. In the laboratory, Enteromorpha

photosynthetic rates (Table 4) were higher than the Ulva photosynthetic rates (Table 5), unlike

what was observed in the in situ experiments in which Ulva presented the highest productivity

values.

Table 3.3 – Average net primary productivity (Pn) of Enteromorpha spp. for each light incubation

experiment. C.V. – variation coefficient.

Irradiance

Enteromorpha Pn

(µE m-2 _s-1_{) Incubated biomass (g dw) Incubated biomass (g dw)}

0.12(⇔ 1 g fw) C.V. (%) 0.48 (⇔ 4 g fw) C.V. (%)

70 2.8 6.3 0.4 63.6

150 7.1 11.3 2.5 3.1

300 7.1 1.2 2.3 13.9

500 8.5 0.3 3.7 2.5

850 9.2 2.2 3.9 3.0

Chapter III

33

Table 3.4 – Average net primary productivity (Pn) of Ulva spp. for each light incubation experiment. C.V. –

variation coefficient.

Irradiance

Ulva Pn

(µE m-2 _s-1_{) Incubated biomass (g dw) Incubated biomass (g dw)}

0.18(⇔ 1 g fw) C.V. (%) 0.67 (⇔ 4 g fw) C.V. (%)

70 0.5 30.4 0.3 23.5

150 5.1 37.2 1.4 4.3

300 3.8 3.2 1.5 7.7

500 4.9 2.5 1.7 5.8

850 7.0 4.1 2.9 1.4

1100 1.0 42.0 0.2 55.3

Comparing both incubation experiments one can notice that, for a similar value of incubated biomass (4 g fw) and at the same range of light irradiances (415 – 850 µE m-2 _s-1_{) the} Enteromorpha productivity values measured in the laboratory (2.3 – 3.9 mg C g-1 dw h-1) were

higher than those measured insitu (0.76 – 1.67 mg C g-1 dw h-1). Conversely, Ulva production rates

were higher in the in situ incubation experiments (3.42 – 4.64 mg C g-1 dw h-1) than in the

laboratory (1.7 - 2.9 mg C g-1 _{dw h}-1_{). These results may be explained by differences in the}

experimental conditions (water temperature and nutrient concentrations) or in the algae physiological state at the time of the experiments (Fong et al., 1994).

4.2.2 Respiratory rates

The seaweed respiratory rates measured in the laboratory are presented in Table 2.5. Correlation analysis revealed that algae respiration was significantly (p<0.01) influenced by the incubated

biomass.

Despite the incubated biomass, Enteromorpha (1.2 mg C g-1 dw h-1) and Ulva (1.4 mg C g-1 dw h-1)

showed similar respiratory rates.

Once again, laboratory and field experiments yielded different results, probably due to different experimental conditions.

Chapter III

34

Table 3.5 – Mean respiratory rates of Enteromorpha and Ulva with different incubated biomasses. C.V. – variation coefficient.

Respiratory rates (mg Cg-1 dw h-1)

Enteromorpha spp. Ulva spp.

Incubated biomass (g dw) Incubated biomass (g dw)

0.12 0.48 0.18 0.67

Average 1.2 0.8 1.4 0.8

C.V. (%) 12.9 10.0 0.7 1.2

4.2.3 P-I curves

Because Steele’s (1962) model did not accurately describe the experimental data (

p

= 1),

only the results concerning the Eilers and Peters (1988) rational model will be presented

and discus